EP2399513B1 - Système pour la surveillance automatique non invasive, la détection, analyse, caractérisation, prédiction ou prévention des crises et symptômes des troubles du mouvement - Google Patents

Système pour la surveillance automatique non invasive, la détection, analyse, caractérisation, prédiction ou prévention des crises et symptômes des troubles du mouvement Download PDF

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EP2399513B1
EP2399513B1 EP10006537.4A EP10006537A EP2399513B1 EP 2399513 B1 EP2399513 B1 EP 2399513B1 EP 10006537 A EP10006537 A EP 10006537A EP 2399513 B1 EP2399513 B1 EP 2399513B1
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signal
data
movement
signals
data acquisition
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German (de)
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EP2399513A1 (fr
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Tamer Khattab
Amr Mohamed
Adnan Abu-Dayya
Basim Uthman
Leopold Streletz
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Qatar University Qstp-B
Cornell University
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Qatar University Qstp-B
Cornell University
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1123—Discriminating type of movement, e.g. walking or running
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1112—Global tracking of patients, e.g. by using GPS
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
    • A61B5/4094—Diagnosing or monitoring seizure diseases, e.g. epilepsy
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4836—Diagnosis combined with treatment in closed-loop systems or methods
    • A61B5/4839—Diagnosis combined with treatment in closed-loop systems or methods combined with drug delivery
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    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/02—Operational features
    • A61B2560/0242—Operational features adapted to measure environmental factors, e.g. temperature, pollution
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
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    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7232—Signal processing specially adapted for physiological signals or for diagnostic purposes involving compression of the physiological signal, e.g. to extend the signal recording period
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching

Definitions

  • the present invention relates to a monitoring system for predicting, detecting, analysing, characterising, preventing or subduing movement disorders or transient neurological disorders, in particular seizures associated with syndromes and diseases, such as epilepsy, Parkinson's disease, severe asthma attacks or other conditions leading to hypoxia, different types of dystonia or dyskinesia, Wilson's disease, Pitt-Hopkins syndrome and others.
  • seizures associated with syndromes and diseases such as epilepsy, Parkinson's disease, severe asthma attacks or other conditions leading to hypoxia, different types of dystonia or dyskinesia, Wilson's disease, Pitt-Hopkins syndrome and others.
  • seizure is used almost exclusively to refer to an epileptic seizure; however, its more general meaning will be used herein, i.e. it will be used to refer to any type of seizure.
  • a movement disorder is a neurological condition that affects the speed, fluency, quality, and ease of movement.
  • Abnormal fluency or speed of movement may involve excessive or involuntary movement (hyperkinesia), slowed or absent voluntary movement (hypokinesia).
  • Movement disorders include the following conditions: ataxia (lack of coordination, often producing jerky movements), dystonia (causes involuntary movement and prolonged muscle contraction), Huntington's disease (also called chronic progressive chorea), multiple system atrophies (e.g., Shy-Drager syndrome), myoclonus (rapid, brief, irregular movement), Parkinson's disease, progressive supranuclear palsy (rare disorder that affects purposeful movement), restless legs syndrome (RLS), reflex sympathetic dystrophy/periodic limb movement disorder (RSD/PLMD), tics (involuntary muscle contractions), Tourette's syndrome, tremor (e.g., essential tremor, resting tremor) and Wilson disease (inherited disorder that causes neurological and psychiatric symptoms and liver disease).
  • a seizure is a sudden episode of transient neurologic symptoms caused by abnormal electrical activity in the brain. Typically, symptoms can include involuntary muscle movements, sensory disturbances or altered consciousness.
  • a movement disorder can differ from one person to another and, in the case of seizures, from episode to episode, it is widely acknowledged that experiencing a short seizure or a life-long movement disorder is debilitating.
  • a seizure may be experienced as a trance-like state from a few seconds up to several minutes or as lost consciousness and convulsions whereas a movement disorder may be experienced as involuntary and prolonged muscle contraction.
  • their occurrence has significant consequences; these can vary from limited ability to participate in certain activities or perform certain tasks, such as driving, to severe impairment and effective incapacitation.
  • WO2007/034476 discloses a device and a method for detecting an epileptic event by attaching a watch-like device on a user's limb, which watch-like device comprises a detector including three accelerometers which determine whether a seizure is occurring by comparing motion signal parameters to non-epileptic and epileptic movement parameters. While this system detects a paroxysmal event once it has started, the system does not predict or prevent it. As a result, the system is capable of evaluating whether the subject requires an alert, assistance or medication only after the event has started. In addition, the system is limited to certain types of seizures due to its dependency on limited types and point of signal collections.
  • prior art systems experience a high rate of false positives when detecting an event.
  • the device and method disclosed in WO2007/034476 only calculates the probability of a seizure occurring at a specific time by using three-dimensional motion detectors; thus, sudden movements may generate a false positive alert.
  • the device and method disclosed therein only detect changes to movement patterns.
  • WO2009/020880 discloses apparatus for diagnosing epileptic and non-epileptic movements of a subject during seizures by using a movement detection device connected to a processor module.
  • Software or firmware algorithms operate within the processor module for describing and characterising the statistical properties of the movement signal during clinical seizure episodes.
  • the system can be used to distinguish the movement features of epileptic seizure events from those of non-epileptic pseudo-seizures.
  • the movement detection device may be an accelerometer, an electromyographic electrode or an optoelectronic distortion sensor.
  • the subject may be placed in a clinical setup in which doctors monitor the apparatus and compare the results obtained from the apparatus with those obtained from an electroencephalogram used in addition to or instead of the apparatus.
  • EP2123221 describes a seizure detection system comprising a motion detector, cameras, a communications line and a processor and/or seizure alert system; the motion detector being an accelerometer or gyro sensor. The system allows differentiation of seizure associated motion from non-seizure associated motion.
  • WO2007/034476 relates to a device and method for detecting and alerting of an epileptic seizure.
  • the device comprises at least one motion sensor adapted to produce an electrical signal corresponding to a mechanical movement of the device, a signal processing unit adapted to compare at least one parameter from a signal output with at least one set of stored sets of motion signal parameters, a communication unit adapted to transmit an alert signal to a remote location and a control circuit adapted to activate the communication unit when the signal output meets a threshold level.
  • WO2002/082999 discloses a method and device for detecting and interpreting the motion of a user from a sequence of images.
  • the device comprises an optical system adapted to capture an image, an image collector adapted to convert the image into an image signal, and a control unit adapted to record a plurality of image signals.
  • An image database adapted to store raw data and an image processor adapted to analyse image data may be connected to the device.
  • the device has a discrimination function for detecting and characterising periodic movement of a bed or wheelchair ridden subject to improve identification of epileptic seizures and non-epileptic events.
  • the present system seeks to provide a system adapted to monitor symptoms associated with movement disorders or seizures, which can then be used to predict, detect, analyse, characterise and or prevent or subdue said symptoms.
  • the present system is capable of accurately monitoring thereby allowing to detect, predict, characterise and classify seizures or movement disorders symptoms. Further, the present system is suitable for analysing, preventing or subduing seizures or movement disorder symptoms and not only of monitoring or detecting them.
  • the present invention detects whether a subject is experiencing other symptoms or environmental factors which are related to or trigger a seizure or movement disorder symptoms by collecting movement data to monitor and prevent movement disorder symptoms or seizures; i.e., the system functions as a unified integrated platform for applications requiring collection of movement and environmental data.
  • the system uses additional sensors, such as humidity sensors, in addition to known physical sensors adapted to measure vital signs.
  • the system may include an input from a on-invasive blood-glucose level measurement apparatus, such as, for example, the apparatus described in WO 2002/082983 .
  • facial reaction and eye movement patterns may be used to detect changes associated with a seizure or symptoms of a movement disorder.
  • the present system allows medical practitioners, users and their families to analyse and classify seizures or movement disorders.
  • the system may also incorporate movement trajectory and posture detection algorithms based on sonar based localisation techniques amplified with acceleration sensors, such as, for example, AliveTech (RTM) sensors, to enhance and broaden the range of movement disorders which can be detected.
  • acceleration sensors such as, for example, AliveTech (RTM) sensors
  • the present invention can acquire data in three different modes: online, offline and hybrid mode.
  • online acquisition mode the system acquires real-time data relating to the user's body and its surrounding environment which is subsequently recorded locally or relayed in real-time; this enables the system to predict, detect and monitor movement disorder symptoms or seizure.
  • offline mode the data is directly or remotely collected from pre-recorded media which is then relayed to the appropriate module or unit; this mode is useful in characterising seizures or movement disorder symptoms of a specific user, i.e. it allows individualisation of standard data.
  • This mode also enables analysis of the acquired data which can subsequently be used for predicting, detecting, preventing or subduing a seizure or movement disorder symptoms. Further, the data can be obtained while the user carries out his normal activities and recorded; as a result, there is no time wastage nor is a medical practitioner's presence required, therefore, the cost of and inconvenience caused by the process are significantly reduced. Finally, when the system is in hybrid acquisition mode the data is retrieved directly from sensors as well as indirectly from pre-recorded media, i.e. the two types of data acquisition are combined.
  • This mode is particularly useful for predicting, preventing and subduing seizures or symptoms because actual data can be compared with user-specific recorded data; thus, the system is capable of predicting likelihood of a seizure or movement disorder symptoms under in a particular environment; in other words, the system uses individualised standard data and user-specific data to predict a seizure or symptoms of a movement disorder in real time and may be used to prevent or subdue them if the user has been prescribed medication which is delivered through the system.
  • seizures or movement disorder symptoms are sometimes triggered or exacerbated by a change in light or temperature, sudden activity, excitement or stress.
  • the system will automatically recognise that the user is susceptible to experience a seizure or symptoms if differences in the environment or the user's vital signs are detected. Further, the system may alert the user or a remote monitoring unit that conditions have changed and that he is more susceptible to experience a seizure or movement disorder symptoms. In addition, the system may deliver prescribed medication to the user automatically under these circumstances.
  • the system may be used to predict asthma-related seizures; if the user suffers from severe attacks, these may be triggered by changes in atmospheric humidity or particular chemicals, such as tobacco and tar smoke (i.e. cigarette smoke). It is also widely accepted that stress may also trigger an attack.
  • the present system monitors humidity, odours and heart rate, it is capable of sending an alert signal to the user or to a remote location when the user is susceptible to experience a severe attack which may eventually lead to a seizure.
  • the system may deliver prescribed medication to the user automatically.
  • the system monitors the environment and sends an alert signal to the user or a remote location in the event the user is deemed likely to experience a seizure.
  • the system is capable of using real time data to predict the likelihood of a seizure or movement disorder symptoms occurring under specific circumstances for a specific user.
  • the monitoring system 1 comprises data acquisition means 2 having at least one movement detector 6, typically accelerometers or gyroscopic methods, recording means 3, typically video recording means 3 and a signal processing unit, typically a CPU or a wireless network, having a central aggregation and relaying module 8 and a processing module 9.
  • the system 1 further comprises other sensors or data acquisition means 7 for environment and additional user data acquisition; for example, a light sensor, a temperature sensor, a humidity sensor, a smell detector, heart rate monitoring means, blood pressure monitoring means, blood-glucose level monitoring means (non-invasive), electrocardiography means, electroencephalography means and acoustic distance measurement devices.
  • the type of sensor will depend at least to some extent on the indication being monitored.
  • the system further comprises location-sensing means 10, such as GPS, WiFi or equivalent positioning means.
  • a plurality of sensors 7 are connected to the subject to acquire data relating to movement and the subject's body (for example, pulse, temperature, respiration rate, blood pressure, blood glucose levels, electrical activity of the brain, as measured by an electroencephalogram (EEG), or the heart, as measured by an electrocardiogram (ECG).
  • EEG electroencephalogram
  • ECG electrocardiogram
  • the sensor output is then recorded locally by the recording means 3 or relayed in real-time to a central data aggregation and relaying module 8 for signal processing.
  • the system 1 acquires data in the offline mode 12, the data is directly or remotely collected from pre-recorded media, such as video recordings of the subject, recorded test data, sound recordings of the patient, environmental data recordings, etc.
  • the sensor output is subsequently relayed to the central aggregation and relaying module 8 for signal processing.
  • data will be retrieved directly from sensors 6, 7 attached to the user as well as indirectly from pre-recoded media.
  • the sensor output and the output of the pre-recorded media are sent to the central aggregation and relaying module 8 which module combines the two types of output to enable the processing module 9 to characterise the data.
  • the data collected is useful in the analysis and classification of movement data; in addition, it plays a substantial role in generating and transmitting an alert signal.
  • the output of the data acquisition means 2 in any of its three functioning modes is then transferred to the central data aggregation and relaying module 8 which combines and pre-processes the collected output to reduce data size and increase its accuracy.
  • This module 8 cross-references sensor or pre-recorded media outputs with a database 14 including standard seizure or movement disorder data and patient-specific data so that data relating to regular movements not associated with seizures or duplicated data is disregarded and it compresses data to ensure further analysis thereof is performed more efficiently.
  • this module relays an aggregated output to the processing module 9 connected to said aggregation and relaying module via an internal connection or a wireless network, which wireless network enables a remote connection over large coverage areas; for example a mobile telephone system, WiFi, WiMax.
  • location information data is collected using GPS systems and/or through other localisation means 10 depending on the technology used for wireless connection between the aggregation and relaying module 8 and the processing module 9. Either method can be used separately or in combination to allow better coverage (indoor and outdoor) and enhanced accuracy.
  • the location sensing means 10 can be incorporated in the data aggregation and relaying module 8.
  • the processing module then processes the signal as described in Figure 2 to generate a result output signal 20 comprising information on whether the movement is symptomatic, asymptomatic or predictive of imminent symptoms; subject-specific information, a proposed course of action and a information on whether an alert is necessary.
  • the processing module 9 sends an alert signal to a generation and propagation module 21.
  • the alert generation and propagation module 21 can be remotely configured by monitoring staff or the user to send an alert or a plurality of alerts, including sound, visual and data alerts or a combination thereof, to designated monitoring means or a predetermined person to inform said monitoring means or predetermined person that the user is either experiencing a seizure or movement disorder symptoms or is under specified conditions defined by the system which make symptoms imminent.
  • alert signal contains information on the type of movement, subject-specific information and a suggested course of action the system either sends an alert to the subject so he can follow the suggested action or, if the system is used with an automated drug delivery control system 22, the alert triggers the automated drug delivery control system 22 so a drug is delivered or treatment dosages readjusted based on the monitored conditions. Further, said alert signal may provide the location of the user.
  • Figure 2 shows the steps followed to process the aggregated output signal.
  • the processing module 9 filters 16 signals to remove background noise and signal artefacts as the data collected from the plurality of sensors 6, 7 or pre-recorded media 3 will include noise signals.
  • the processing module 9 removes background noise signals, both user-specific and environmental, from input signals collected from different sensors 6, 7 by removing signals corresponding to predetermined frequencies and speeds. Further, noise resulting from imperfections in the sensing process are also removed at this stage. It has been observed that epileptic fit spans have a characteristic frequency and speed of motion.
  • the present system filters time domain, speed domain and frequency domain to remove unwanted signals by applying knowledge on expected data through the use of a combination of threshold based and correlation based methods to filter out undesired signals, i.e. it compares the time, speed and frequency of movement in the aggregated signals against a database including voluntary movement data. This process enhances the accuracy of the eventual actions initiated by the system.
  • the aggregated signal output is correlated with standard data and patient-specific data stored in a database to establish a movement threshold; alternatively, a predetermined threshold may be used. If any signal included in the output is under the applied threshold it is filtered out whereas if the signal exceeds the applied threshold it continues to the following processing step.
  • the processing module processes and analyses signals to expose important features or properties of the aggregated data 17.
  • various signal transformation methods in particular, fractional Fourier transform, wavelet transform or short-time Fourier transform are applied to the signals to expose dominant frequencies, statistical correlations and time-frequency correlations, in other words, the transform methods allow the system to determine whether the signal correlates to the characteristics of a seizure or movement disorder.
  • additional filtering will remove noise signals which could not previously be separated from data signals; only the transformed data signals will proceed to the subsequent step.
  • the processing units recognises symptomatic movement patterns in the signals 18 by applying dynamically adaptive customized fuzzy logic algorithms with tuneable parameters to the filtered and processed signals; which parameters are set by using standard movement data, user specific data, and environmental data to perform movement pattern recognition 18 and classification of movement signals into predefined categories obtained by analysing standard seizure or disorder data and user specific data.
  • the processing module 9 provides the system with parameters for the fuzzy logic algorithms and the predefined categories dynamically, i.e. in real time.
  • the processing module allows the signals to be analysed and classified 19 by using machine-learning algorithms which use training, learning and medical knowledge based data 14 to allow fine-tuning of the classification process 19.
  • the data processing module 9 applies machine-learning algorithms to the analysed and classified signals (i.e. the signals received from the pattern recognition step) with the purpose of deciding whether the data is associated with a medical condition 19.
  • machine-learning algorithms utilise medical databases 14, historical data in the recording means 3, and the classified signal data generated by the pattern recognition step 18 to generate a result which indicates whether the signals from the monitored movement data are associated with a medical condition.
  • the processing module 9 detects or predicts movements associated with seizures or movement disorders by using a wavelet basis extracted from a learned movement model, which analyses time, speed and frequency signals to determine whether the movement under analysis is associated with seizures, or movement disorders.
  • the algorithm then analyses the difference between predetermined upper and lower energy correlation thresholds to detect symptomatic movements. If the signals from the observed movement result in a difference between the predetermined thresholds which exceeds predefined values, the observed movement is classified as symptomatic whereas if the difference does not exceed predetermined values but is within another predefined range, the observed movement is classified as voluntary movement.
  • the system is further adapted to identify differences which correlate to a developing seizure or symptomatic episode.
  • the processing module 9 will correlate the movement signal to the medical database medical data 14, historical database 3, and classified signal data generated by the pattern recognition step 18 and will generate a result output signal 20 comprising information on whether the movement is symptomatic, asymptomatic or predictive of imminent symptoms; subject-specific information, a proposed course of action and a information on whether an alert is necessary.
  • the processing module 9 will send an alert signal to a generation and propagation module.
  • the alert generation and propagation module can be remotely configured by monitoring staff or the user to send an alert or a plurality of alerts, including sound, visual and data alerts or a combination thereof, to designated monitoring means or a predetermined person to inform said monitoring means or predetermined person that the user is either experiencing a seizure or movement disorder symptoms or is under specified conditions defined by the system which make symptoms imminent.
  • alert signal contains information on the type of movement, subject-specific information and a suggested course of action the system either sends an alert to the subject so he can follow the suggested action or, if the system is used with an automated drug delivery control system, the alert triggers the automated drug delivery control system so a drug is delivered or treatment dosages readjusted based on the monitored conditions. Further, said alert signal may provide the location of the user.
  • the output signal is also used by the system as tuning feedback output; thus, it is sent back to the processing module 9 to maintain or improve fuzzy logic parameters and enhance classification accuracy during the pattern recognition step 18.
  • the system of the present invention successfully detected 92 episodes out of 100. Taking into account variations of movement and differences between patients, the overall rate of detection was found to be approximately 90%. The same trials, established that the system experiences a false alarm probability of around 15% when considering fast, repetitive movements caused by activities, such as, throwing a ball, playing tennis, running, jogging, or similar actions. Thus, the present system provides a clear advantage over prior art devices.

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Claims (13)

  1. Un système de surveillance destiné à suivre les mouvements d'un utilisateur et comprenant des moyens d'acquisition de données (2), des moyens d'enregistrement (3) et une unité de traitement de signaux ;
    dans lequel lesdits moyens d'acquisition de données (2) comportent au moins un détecteur de mouvement (6) afin de détecter les données de mouvement d'un utilisateur et de générer un signal de mouvement, les caractéristiques de ce signal de mouvement dépendant des caractéristiques de mouvement de l'utilisateur ;
    dans lequel ladite unité de traitement de signaux est configurée pour recevoir ledit signal de mouvement, pour filtrer les signaux de bruit et les signaux de mouvement volontaire provenant de ce signal de mouvement afin de générer un signal filtré, et pour analyser ce signal filtré afin de reconnaître les schémas de mouvements symptomatiques ;
    les moyens d'acquisition de données (2) comprenant également d'autres capteurs (7) destinés à détecter des données supplémentaires susceptibles de refléter d'autres symptômes de l'utilisateur ou des facteurs environnementaux liés à, ou susceptibles de déclencher une crise d'épilepsie ou des symptômes de troubles du mouvement, et à générer les signaux supplémentaires correspondants ;
    l'unité de traitement de signaux étant en outre configurée pour utiliser lesdites données supplémentaires afin de générer un signal sortant de résultat (20) prévoyant la probabilité de survenue d'une crise d'épilepsie ou de symptômes de troubles du mouvement.
  2. Un système selon la revendication 1, dans lequel l'unité de traitement (2) comprend en outre un module central d'agrégation et de relais (8) et un module de traitement (9) ; ledit module central d'agrégation et de relais (8) étant configuré pour combiner et prétraiter les signaux générés par les moyens d'acquisition de données (2) et pour transmettre un signal agrégé (16) au module de traitement (9), et ledit module de traitement étant configuré pour traiter le signal agrégé (16) afin de générer le signal sortant de résultat (20).
  3. Un système selon la revendication 2, dans lequel les moyens d'acquisition de données (2) sont configurés pour fonctionner en mode en ligne (11) ; dans ce mode en ligne (11), les moyens d'acquisition de données (2) sont connectés à un utilisateur afin d'acquérir des données et produire des signaux en ligne, lesquels sont enregistrés localement par des moyens d'enregistrement (3) ou transmis en temps réel au module central d'agrégation et de relais de données (8) en vue du traitement des signaux.
  4. Un système selon la revendication 2, dans lequel lesdits moyens d'acquisition de données (2) sont configurés pour fonctionner en mode hors ligne (12) ; dans ce mode hors ligne (12), les données sont collectées directement à partir des moyens d'enregistrement (3) ou à distance de ceux-ci afin de produire des signaux hors ligne, lesquels sont transmis au module central d'agrégation et de relais (8) en vue de leur traitement.
  5. Un système selon la revendication 2, dans lequel les moyens d'acquisition de données (2) sont configurés pour fonctionner en mode hybride (13) ; dans ce mode hybride (13), les données sont récupérées directement à partir desdits moyens d'acquisition de données (2) fixés sur un utilisateur et indirectement à partir des moyens d'enregistrement (3) afin de produire des signaux hybrides composés de signaux en ligne et hors ligne, lesquels sont envoyés vers le module central d'agrégation et de relais (8) en vue de leur traitement.
  6. Un système selon l'une quelconque des revendications 2 à 5, lorsqu'elles dépendent de la revendication 2, dans lequel les moyens d'acquisition de données (2) ou le module d'agrégation et de relais de données (8) comprennent des moyens de localisation (10) de type GPS, WiFi ou équivalents.
  7. Un système selon l'une quelconque des revendications précédentes, dans lequel les capteurs supplémentaires (7) sont sélectionnés parmi les suivants : un capteur de lumière, un capteur de température, un capteur d'humidité, un capteur d'odeurs, des moyens de suivi du rythme cardiaque, des moyens de suivi de la tension artérielle, des moyens de suivi du taux de glycémie non invasif, des moyens d'électrocardiographie, des moyens d'électroencéphalographie ou un dispositif de mesure de la distance acoustique.
  8. Un système selon l'une quelconque des revendications 2 à 7, lorsqu'elles dépendent de la revendication 2, dans lequel le module central d'agrégation et de relais de données (8) est en outre configuré pour compresser les signaux générés par les moyens d'acquisition de données (2) et pour améliorer la précision du résultat en excluant les données relatives aux mouvements volontaires normaux et les données en double.
  9. Un système selon la réclamation 8, dans lequel le signal est filtré par domaine temporel et fréquentiel afin d'y reconnaître les schémas associés à un état prédéterminé.
  10. Un système selon les revendications 8 ou 9, dans lequel le module de traitement (9) est configuré pour reconnaître des schémas dans le signal agrégé en utilisant des méthodes de logique floue ou des algorithmes d'apprentissage automatique ; ces algorithmes analysent la différence entre des seuils de corrélation énergétique inférieur et supérieur prédéterminés afin de caractériser et classifier ledit signal agrégé, détecter ou prédire des mouvements prédéterminés et générer un signal sortant de résultat (20) qui indique si les signaux générés par les moyens d'acquisition de données (2) sont associés à un état prédéterminé.
  11. Un système selon l'une quelconque des revendications 2 à 10, lorsqu'elles dépendent de la revendication 2, dans lequel le module de traitement (9) est en outre configuré pour générer un signal d'alerte, lequel est transmis à un module de génération et de propagation d'alerte (21), qui est configuré pour envoyer une ou plusieurs alertes vers des moyens de suivi à distance désignés ou une personne prédéterminée lorsque les signaux générés par les moyens d'acquisition de données (2) sont associés à un état prédéterminé.
  12. Un système selon la revendication 11, dans lequel le signal d'alerte indique la localisation de l'utilisateur.
  13. Un système selon l'une quelconque des revendications précédentes, qui comprend en outre un système d'administration de médicaments automatique (22) qui, en cas de détection ou de prédiction d'une crise d'épilepsie ou de symptômes de troubles du mouvement, envoie des instructions au système d'administration de médicaments automatique (22) pour permettre l'administration d'une dose appropriée.
EP10006537.4A 2010-06-23 2010-06-23 Système pour la surveillance automatique non invasive, la détection, analyse, caractérisation, prédiction ou prévention des crises et symptômes des troubles du mouvement Not-in-force EP2399513B1 (fr)

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